Accurate 3D human pose estimation is essential for sports analytics,
coaching, and injury prevention. However, existing datasets for monocular pose
estimation do not adequately capture the challenging and dynamic nature of
sports movements. In response, we introduce SportsPose, a large-scale 3D human
pose dataset consisting of highly dynamic sports movements. With more than
176,000 3D poses from 24 different subjects performing 5 different sports
activities, SportsPose provides a diverse and comprehensive set of 3D poses
that reflect the complex and dynamic nature of sports movements. Contrary to
other markerless datasets we have quantitatively evaluated the precision of
SportsPose by comparing our poses with a commercial marker-based system and
achieve a mean error of 34.5 mm across all evaluation sequences. This is
comparable to the error reported on the commonly used 3DPW dataset. We further
introduce a new metric, local movement, which describes the movement of the
wrist and ankle joints in relation to the body. With this, we show that
SportsPose contains more movement than the Human3.6M and 3DPW datasets in these
extremum joints, indicating that our movements are more dynamic. The dataset
with accompanying code can be downloaded from our website. We hope that
SportsPose will allow researchers and practitioners to develop and evaluate
more effective models for the analysis of sports performance and injury
prevention. With its realistic and diverse dataset, SportsPose provides a
valuable resource for advancing the state-of-the-art in pose estimation in
sports.

SportsPose 是一个大规模的姿态数据集，包括高度动态的运动。使用超过 176,000 个来自 24 名不同受试者的 5 种运动活动的 3D 姿势，SportsPose 提供了一组反映了运动复杂和动态性的多样化和全面的 3D 姿势。数据集伴随代码，可以从我们的网站上下载，希望 SportsPose 可用于开发和评估更有效的运动表现分析和损伤预防模型。